Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data
Open accessAbstract
Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.
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BlueDot appears to have been the first to notice the epidemic in Wuhan, many hours before Chinese officials and other foreign organizations and agencies.
“BlueDot, a Toronto-based start-up that uses an AI-enhanced surveillance gadget, appears to have been the first to notice the epidemic in Wuhan, many hours before Chinese officials and other foreign organizations and agencies (Conway et al. 2019).”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Conway et al. 2019
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Adams, N., Artigiani, E., & Wish, E. D. (2019) · Journal of Drug Issues · doi:10.1177/0022042619833911 · Cited by 20 · see citing works
Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet
Guo, H., Xu, N., Hou, L., & Li, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7156 · Cited by 19 · see citing works
Detection and Analysis of Drug Misuses. A Study Based on Social Media Messages
Bigeard, É., Grabar, N., & Thiessard, F. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00791 · Cited by 15 · see citing works
Ayers, J. W., Dredze, M. H., Leas, E. C., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205822 · Cited by 15 · see citing works
Cataloguing Treatments Discussed and Used in Online Autism Communities
Zhang, S., Kang, T., Qiu, L., Zhang, W., Yu, Y. H., & Elhadad, N. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052661 · Cited by 11 · see citing works
Choosing your platform for social media drug research and improving your keyword filter list
Adams, N., Artigiani, E., & Wish, E. D. (2019) · University Libraries (University of Maryland) · doi:10.13016/ompk-g8pr · Cited by 6 · see citing works
Text Analysis and Automatic Triage of Posts in a Mental Health Forum
Asgari, E., Nasiriany, S., & Mofrad, M. R. K. (2016) · doi:10.18653/v1/w16-0318 · Cited by 3 · see citing works
Text-based experiments for Predicting mental health emergencies in online web forum posts
Franco-Penya, H.-H., & Sanchez, L. M. (2016) · doi:10.18653/v1/w16-0327 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
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Cited by
Sorted by citations
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281 · see citing works
Artificial Intelligence in mental health and the biases of language based models
Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164 · see citing works
The use of social media for health research purposes: scoping review
Bour, C., Ahne, A., Schmitz, S., Perchoux, C., Dessenne, C., & Fagherazzi, G. (2021) · Journal of Medical Internet Research · doi:10.2196/25736 · Cited by 56 · see citing works
Fang, C., Markuzon, N., Patel, N., & Rueda, J.-D. (2022) · Value in Health · doi:10.1016/j.jval.2022.06.004 · Cited by 36 · see citing works
- Authors
- Conway, M., Hu, M., & Chapman, W. W.
- Affiliations
- University of Utah (US)
- Corresponding author
- Conway, Mike
- Venue
- Yearbook of Medical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2019-08
- Volume
- 28
- Issue
- 01
- Pages
- 208-217
- ISSN
- 0943-4747 (ISSN-L)
- 2364-0502
- PMC
- PMC6697505
- OpenAlex
- W2968719776
- Cited by
- 91
- References
- 112
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 11 · 2023: 13 · 2022: 18 · 2021: 22 · 2020: 11 · 2019: 3 · 2018: 1
- FWCI
- 16.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Yearbook of Medical Informatics (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health
- National Institute on Drug Abuse (R21DA043775)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- natural language processing, social media, public health, online health communities, Twitter, Reddit, Facebook, infectious diseases, influenza surveillance, dengue, mental health, substance use, substance abuse, lexicon-based methods, deep neural network, classical machine learning
- MeSH terms
- Patient Generated Health Data, Health Services Research, Humans, Natural Language Processing, Public Health, Bibliometrics, Social Media, Public Health Surveillance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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